{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAABECAYAAACmjMM7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAACPElEQVR4nO3ZMYoTUQDG8ZdNSAJZdr1FzmApeBl7QVAP4DE8glhoIWxn4RFi5xViwJhMMhaxy2ZB/F7eEn6/dhj4qvnPmxn0fV8A4H9dtR4AwGUQFAAiBAWACEEBIGJ06sJ8Pu/KITjL880B4JG7KaXsF4vFUT9OBqUcYjKYTsa31WY1tutaL6irG+1bT6jqerBpPaGa1W7WekJV3XrVekJV09mk9YRqtuuulBNftx4KynI6Gd++fvWiyqjH4PuXyz583T3/0XpCVXdP3reeUM3Tr59bT6jq27tnrSdU9ebjy9YTqvnw9lPZrrt7H57+oQAQISgARAgKABGCAkCEoAAQISgARAgKABGCAkCEoAAQISgARAgKABGCAkCEoAAQISgARAgKABGCAkCEoAAQISgARAgKABGCAkCEoAAQISgARAgKABGCAkCEoAAQISgARAgKABGCAkCEoAAQISgARAgKABGCAkCEoAAQISgARAgKABGCAkCEoAAQISgARAgKABGCAkCEoAAQISgARAgKABGCAkCEoAAQISgARAgKABGCAkCEoAAQISgARAgKABGCAkDEoO/7ey/M5/N9KWUwnYzPu+iMdl3rBXV1o33rCVVdDzatJ1Sz2s1aT6iqW69aT6hqOpu0nlDNdt2VUkq/WCyODiSjB+7bl1Ku1r83y1rDqGzXekBdPy/6gP2r9YCqhsNh6wlV/X3oXqqbcujDkZMnFAD4F5f8igfAGQkKABGCAkCEoAAQISgARPwB8K5NVoTYsCUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.ticker as ticker\n",
    "import statsmodels.formula.api as smf\n",
    "from pprint import pprint\n",
    "import re as re\n",
    "\n",
    "from statsmodels.regression.mixed_linear_model import MixedLMResults\n",
    "import networkx as nx\n",
    "\n",
    "import scipy as sp\n",
    "#from scipy.stats import nanmean\n",
    "#from scipy.stats import nanstd\n",
    "import copy\n",
    "import scipy.stats as stats\n",
    "import string\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib\n",
    "\n",
    "from pprint import pprint\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\", context=\"talk\")\n",
    "custom_palette = [ \"greyish\", \"dusty purple\", \"greenish\", \"amber\", \"windows blue\", \"black\",\"faded green\"]  \n",
    "                     #\"green blue\", \"dull green\", \"faded green\",  \n",
    "sns.set_palette(sns.xkcd_palette(custom_palette))\n",
    "current_palette = sns.color_palette()\n",
    "%matplotlib inline\n",
    "sns.palplot(current_palette)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "round_data = pd.read_csv('./all_studies_round_data.csv')\n",
    "#round_data = round_data[round_data.round_index > 3]\n",
    "#print(round_data.size)\n",
    "conditions = ['dynamic_full_feedback',\n",
    "              'dynamic',         \n",
    "              'dynamic_self_feedback',\n",
    "              'dynamic_no_feedback',\n",
    "              'static',\n",
    "              'solo_feedback',\n",
    "              'solo_no_feedback'\n",
    "             ]\n",
    "\n",
    "\n",
    "\n",
    "colors ={'dynamic_no_feedback':'#196FFF',\n",
    "         'dynamic_self_feedback':'#000000',\n",
    "         'dynamic_full_feedback':'#E89468',\n",
    "         'dynamic': '#81B200',\n",
    "         'static':'#9B59B6',\n",
    "         'solo_feedback': '#95A5A6',\n",
    "         'solo_no_feedback': '#95A5A6'\n",
    "        }\n",
    "\n",
    "\n",
    "linestyles ={'dynamic_no_feedback':'-.',\n",
    "         'dynamic_self_feedback':':',\n",
    "         'dynamic_full_feedback':'-',\n",
    "         'dynamic': '-',\n",
    "         'static':'--',\n",
    "         'solo_feedback': '-',\n",
    "         'solo_no_feedback': '-'\n",
    "        }\n",
    "\n",
    "\n",
    "markers ={'dynamic_no_feedback':'d',\n",
    "         'dynamic_self_feedback':'^',\n",
    "         'dynamic_full_feedback':'p',\n",
    "         'dynamic': 'o',\n",
    "         'static':'*',\n",
    "         'solo_feedback': 'H',\n",
    "         'solo_no_feedback': 'h'\n",
    "        }\n",
    "\n",
    "\n",
    "#markers = ['s','o','*']\n",
    "#linestyles = ['--','-',':','-.']\n",
    "tick_size = 25\n",
    "label_size = 35\n",
    "\n",
    "\n",
    "network_conditions = ['dynamic_full_feedback','dynamic','dynamic_no_feedback','dynamic_self_feedback','static']\n",
    "\n",
    "studies = [1,2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['game_id', 'condition', 'round_index', 'correct_answer',\n",
       "       'plot_sequence', 'player_id', 'cumulative_score', 'difficulty',\n",
       "       'revised_guess', 'revised_error', 'alter_1', 'alter_2', 'alter_3',\n",
       "       'increment', 'increment_color', 'in_degree', 'study',\n",
       "       'alter_1_revised_guess', 'alter_2_revised_guess',\n",
       "       'alter_3_revised_guess', 'alter_1_independent_guess',\n",
       "       'alter_2_independent_guess', 'alter_3_independent_guess',\n",
       "       'round_after_shock', 'quarter', 'task_instance_avg_error',\n",
       "       'revised_error_relative2solo', 'independent_error_relative2solo',\n",
       "       'independent_guess', 'independent_error', 'half', 'best_player',\n",
       "       'ind_score', 'cumulative_ind_score', 'final_score', 'final_ind_score',\n",
       "       'improvement', 'group', 'best_player_initial', 'best_player_revised'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_network(game_id, condition):\n",
    "    networks={}\n",
    "    for round_index in range(1,20+1):\n",
    "        networks[round_index] = nx.DiGraph(correct_answer=round_data[round_data.round_index==round_index].correct_answer.iloc[0],\n",
    "                                          condition=condition,game_id = game_id)\n",
    "    for i,row in round_data[(round_data.game_id==game_id) & (round_data.condition == condition)].iterrows():\n",
    "        networks[row.round_index].add_node(row.player_id,name=row.player_id) \n",
    "        networks[row.round_index].nodes[row.player_id]['cumulative_score']= row.cumulative_score\n",
    "        networks[row.round_index].nodes[row.player_id]['independent_guess']= row.independent_guess\n",
    "        networks[row.round_index].nodes[row.player_id]['independent_error']= row.independent_error\n",
    "        networks[row.round_index].nodes[row.player_id]['revised_guess']= row.revised_guess\n",
    "        networks[row.round_index].nodes[row.player_id]['increment']= row.increment\n",
    "        networks[row.round_index].nodes[row.player_id]['revised_error']= row.revised_error\n",
    "        networks[row.round_index].nodes[row.player_id]['shape']= 'o'\n",
    "        #networks[row.round_index].node[row.player_id]['color']= row.cumulative_color\n",
    "        \n",
    "        lst = list(round_data[(round_data.game_id==game_id) & (round_data.condition==condition) &\n",
    "                              (round_data.round_index==row.round_index)]['cumulative_score'])        \n",
    "\n",
    "        norm = matplotlib.colors.Normalize(vmin=min(lst), vmax=max(lst))\n",
    "        #norm = matplotlib.colors.Normalize(vmin=0, vmax=1)\n",
    "        mapper = cm.ScalarMappable(norm=norm, cmap=cm.RdYlGn)\n",
    "        networks[row.round_index].nodes[row.player_id]['gradient_color']=mapper.to_rgba(row.cumulative_score)\n",
    "\n",
    "        networks[row.round_index].nodes[row.player_id]['size']= 140 + (row.in_degree**1.4)*500 if row.in_degree > 0 else 200\n",
    "        for i in range(1,3+1):\n",
    "            if isinstance(row['alter_'+str(i)],str): #in case someone follows less than 3\n",
    "                networks[row.round_index].add_edge(row.player_id, row['alter_'+str(i)])\n",
    "    return networks\n",
    "\n",
    "\n",
    "\n",
    "def freeman_centralization(G):\n",
    "    N=G.order()\n",
    "    indegrees = list(dict(G.in_degree()).values())\n",
    "    #print('indegrees',indegrees)\n",
    "    max_in = max(indegrees)\n",
    "    return float((N*max_in - sum(indegrees)))/(N-1)**2\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "def centrality_performance_correlation(G):\n",
    "    performance = []\n",
    "    centralities = []\n",
    "    try:\n",
    "        centrality = nx.eigenvector_centrality(G)\n",
    "    except:\n",
    "        return np.nan\n",
    "    #print(centrality)\n",
    "\n",
    "    for node in list(dict(G.nodes.data()).values()):\n",
    "        #print(G.in_degree(node['name']))\n",
    "        centralities.append(centrality[node['name']])\n",
    "        performance.append(node['cumulative_score']) ##this is the origianl measure\n",
    "        #performance.append(node['independent_error']) ##this is trying things out\n",
    "        \n",
    "    #print(indegrees)\n",
    "    correlation = pd.DataFrame({'centralities': centralities,\n",
    "                   'performance': performance}).corr().centralities.performance\n",
    "\n",
    "    return 0 if np.isnan(correlation) else correlation\n",
    "\n",
    "def degree_performance_correlation(G):\n",
    "    performance = []\n",
    "    indegrees = []\n",
    "    for node in list(dict(G.nodes.data()).values()):\n",
    "        #print(G.in_degree(node['name']))\n",
    "        indegrees.append(G.in_degree(node['name']))\n",
    "        performance.append(node['cumulative_score']) ##this is the origianl measure\n",
    "        #performance.append(node['independent_error']) ##this is trying things out\n",
    "        \n",
    "    #print(indegrees)\n",
    "    correlation = pd.DataFrame({'indegrees': indegrees,\n",
    "                   'performance': performance}).corr().indegrees.performance\n",
    "\n",
    "    return 0 if np.isnan(correlation) else correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating network data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "network_cent = {'round_index':[],\n",
    "                'freeman_centralization':[],\n",
    "                'condition':[], 'game_id':[], 'change_frequency':[]}\n",
    "networks = {}\n",
    "\n",
    "for condition in conditions:\n",
    "    networks[condition] = []\n",
    "\n",
    "for condition in network_conditions:\n",
    "    game_ids = round_data[round_data.condition==condition].game_id.unique()\n",
    "    for game_id in game_ids:\n",
    "        G = create_network(game_id, condition)\n",
    "        networks[condition].append(G)\n",
    "        for rid in G.keys():\n",
    "            network_cent['freeman_centralization'].append(freeman_centralization(G[rid]))\n",
    "            network_cent['round_index'].append(rid+1)\n",
    "            network_cent['condition'].append(condition)\n",
    "            network_cent['game_id'].append(game_id)\n",
    "            try:\n",
    "                network_cent['change_frequency'].append(len(nx.difference(G[rid],G[rid-1]).edges())) ##how many edges changed\n",
    "            except:\n",
    "                network_cent['change_frequency'].append(None)\n",
    "                \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "centerlization_data = pd.DataFrame.from_dict(network_cent)\n",
    "centerlization_data['quarter'] = None\n",
    "centerlization_data.loc[centerlization_data['round_index'] < 6, 'quarter'] = 1\n",
    "centerlization_data.loc[(centerlization_data['round_index'] >= 6) &\n",
    "                                          (centerlization_data['round_index'] < 11), 'quarter'] = 2\n",
    "centerlization_data.loc[(centerlization_data['round_index'] >= 11) & \n",
    "                                          (centerlization_data['round_index'] < 16), 'quarter'] = 3\n",
    "centerlization_data.loc[centerlization_data['round_index'] >= 16, 'quarter'] = 4\n",
    "\n",
    "\n",
    "centerlization_data['half'] = None\n",
    "centerlization_data.loc[centerlization_data.round_index < 11, 'half'] = 1\n",
    "centerlization_data.loc[centerlization_data.round_index > 10, 'half'] = 2\n",
    "#centerlization_data\n",
    "\n",
    "centerlization_data['adapted'] = False\n",
    "centerlization_data.loc[centerlization_data['quarter'].isin([2,4]), 'adapted'] = True\n",
    "centerlization_data['freeman_centralization'] = centerlization_data['freeman_centralization'].astype(np.float64)\n",
    "centerlization_data['quarter'] = centerlization_data['quarter'].astype(np.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Popularity and Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dynamic_full_feedback p #E89468 -\n",
      "dynamic o #81B200 -\n",
      "dynamic_no_feedback d #196FFF -.\n",
      "dynamic_self_feedback ^ #000000 :\n",
      "static * #9B59B6 --\n",
      "dynamic_full_feedback p #E89468 -\n",
      "dynamic o #81B200 -\n",
      "dynamic_no_feedback d #196FFF -.\n",
      "dynamic_self_feedback ^ #000000 :\n",
      "static * #9B59B6 --\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_formatter = ticker.FixedFormatter([\n",
    "    \"[1-5]\", \"[6-10]\", \"[11-15]\", \"[16-20]\"])\n",
    "x_locator = ticker.FixedLocator([1, 2, 3, 4])\n",
    "\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(12,4))\n",
    "ax = fig.add_subplot(111)\n",
    "for half in range(1,2+1):\n",
    "    for condition in network_conditions:\n",
    "        print(condition,markers[condition], colors[condition],linestyles[condition])\n",
    "        sns.regplot(x=\"quarter\",\n",
    "                    y=\"freeman_centralization\",\n",
    "                    data=centerlization_data[(centerlization_data.half==half)\n",
    "                                             & (centerlization_data.condition==condition)],\n",
    "                    x_estimator=np.mean,\n",
    "                    ax=ax,\n",
    "                    truncate=True,\n",
    "                    marker=markers[condition],\n",
    "                    color=colors[condition],\n",
    "                    line_kws = {'linestyle':linestyles[condition]},\n",
    "                    scatter_kws ={'s':600},\n",
    "\n",
    "                    fit_reg=True,\n",
    "                    ci=95,\n",
    "                    label=condition,\n",
    "                   )\n",
    "\n",
    "ax.set_xlabel(\"\",fontsize=label_size)\n",
    "ax.set_title('Freeman centralization',fontsize=label_size)\n",
    "ax.set_ylabel(\"\")\n",
    "#ax.set_xlim(0.5,4.5)\n",
    "ax.set_ylim(-0.09,0.99)\n",
    "\n",
    "ax.xaxis.set_major_formatter(x_formatter)\n",
    "ax.xaxis.set_major_locator(x_locator)\n",
    "\n",
    "\n",
    "ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "ax.yaxis.set_major_locator(ticker.MultipleLocator(0.1))\n",
    "\n",
    "ax.tick_params(labelsize=tick_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Mixed Linear Model Regression Results\n",
      "====================================================================\n",
      "Model:            MixedLM Dependent Variable: freeman_centralization\n",
      "No. Observations: 200     Method:             REML                  \n",
      "No. Groups:       20      Scale:              0.0066                \n",
      "Min. group size:  10      Log-Likelihood:     184.7287              \n",
      "Max. group size:  10      Converged:          Yes                   \n",
      "Mean group size:  10.0                                              \n",
      "----------------------------------------------------------------------\n",
      "                  Coef.    Std.Err.     z      P>|z|   [0.025   0.975]\n",
      "----------------------------------------------------------------------\n",
      "Intercept          0.580      0.025   23.652   0.000    0.532    0.628\n",
      "adapted[T.True]   -0.046      0.011   -4.042   0.000   -0.069   -0.024\n",
      "Group Var          0.011      0.048                                   \n",
      "====================================================================\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td> <td>freeman_centralization</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>   <td>200</td>         <td>Method:</td>                <td>REML</td>         \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>20</td>          <td>Scale:</td>                <td>0.0066</td>        \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>    <td>10</td>      <td>Log-Likelihood:</td>          <td>184.7287</td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>    <td>10</td>        <td>Converged:</td>                <td>Yes</td>         \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>   <td>10.0</td>            <td></td>                      <td></td>           \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>          <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>   <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>        <td>0.580</td>   <td>0.025</td>  <td>23.652</td> <td>0.000</td>  <td>0.532</td>  <td>0.628</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>adapted[T.True]</th> <td>-0.046</td>   <td>0.011</td>  <td>-4.042</td> <td>0.000</td> <td>-0.069</td> <td>-0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Group Var</th>        <td>0.011</td>   <td>0.048</td>     <td></td>      <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "               Mixed Linear Model Regression Results\n",
       "====================================================================\n",
       "Model:            MixedLM Dependent Variable: freeman_centralization\n",
       "No. Observations: 200     Method:             REML                  \n",
       "No. Groups:       20      Scale:              0.0066                \n",
       "Min. group size:  10      Log-Likelihood:     184.7287              \n",
       "Max. group size:  10      Converged:          Yes                   \n",
       "Mean group size:  10.0                                              \n",
       "----------------------------------------------------------------------\n",
       "                  Coef.    Std.Err.     z      P>|z|   [0.025   0.975]\n",
       "----------------------------------------------------------------------\n",
       "Intercept          0.580      0.025   23.652   0.000    0.532    0.628\n",
       "adapted[T.True]   -0.046      0.011   -4.042   0.000   -0.069   -0.024\n",
       "Group Var          0.011      0.048                                   \n",
       "====================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=centerlization_data[(centerlization_data.condition.isin(['dynamic','full_feedback'])) & \n",
    "                         (centerlization_data.quarter.isin([2,3]))\n",
    "                        ]\n",
    "forumla = \"freeman_centralization ~ adapted\"\n",
    "model = smf.mixedlm(forumla,\n",
    "                    data=data,\n",
    "                    groups=data['game_id']).fit(method='powell')\n",
    "print(model.summary())\n",
    "\n",
    "\n",
    "\n",
    "# for quarter in range(1,4+1):\n",
    "#     print(\"quarter\", quarter)\n",
    "#     data = centerlization_data[(centerlization_data.condition != 'static') \n",
    "#                                               & (centerlization_data.quarter==quarter)]\n",
    "#     forumla = \"freeman_centralization ~ round_index\"\n",
    "#     model = smf.mixedlm(forumla,\n",
    "#                         data=data,\n",
    "#                         groups=data['game_id']).fit(method='powell')\n",
    "#     print(model.summary())\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Score / Popularity Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "network_score_degree_corr = {'round_index':[],'score_degree_corr':[], 'condition':[], 'game_id':[]}\n",
    "\n",
    "for condition in conditions:\n",
    "    for G in networks[condition]:\n",
    "        for rid in G.keys():\n",
    "            network_score_degree_corr['score_degree_corr'].append(degree_performance_correlation(G[rid]))\n",
    "            network_score_degree_corr['round_index'].append(rid)\n",
    "            network_score_degree_corr['condition'].append(condition)\n",
    "            network_score_degree_corr['game_id'].append(G[rid].graph['game_id'])\n",
    "            \n",
    "popularity_data = pd.DataFrame.from_dict(network_score_degree_corr)\n",
    "popularity_data['round_after_shock'] = popularity_data.round_index\n",
    "popularity_data['round_after_shock'] = np.where(popularity_data['round_index'] > 10 ,\n",
    "                                                popularity_data['round_index']-10,\n",
    "                                                popularity_data['round_after_shock']) \n",
    "\n",
    "\n",
    "popularity_data['quarter'] = None\n",
    "popularity_data.loc[popularity_data['round_index'] < 6, 'quarter'] = 1\n",
    "popularity_data.loc[(popularity_data['round_index'] >= 6) &\n",
    "                                          (popularity_data['round_index'] < 11), 'quarter'] = 2\n",
    "popularity_data.loc[(popularity_data['round_index'] >= 11) & \n",
    "                                          (popularity_data['round_index'] < 16), 'quarter'] = 3\n",
    "popularity_data.loc[popularity_data['round_index'] >= 16, 'quarter'] = 4\n",
    "\n",
    "\n",
    "popularity_data['half'] = None\n",
    "popularity_data.loc[popularity_data.round_index < 11, 'half'] = 1\n",
    "popularity_data.loc[popularity_data.round_index > 10, 'half'] = 2\n",
    "popularity_data['quarter'] = popularity_data['quarter'].astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dynamic_full_feedback p #E89468 -\n",
      "dynamic o #81B200 -\n",
      "dynamic_no_feedback d #196FFF -.\n",
      "dynamic_self_feedback ^ #000000 :\n",
      "static * #9B59B6 --\n",
      "dynamic_full_feedback p #E89468 -\n",
      "dynamic o #81B200 -\n",
      "dynamic_no_feedback d #196FFF -.\n",
      "dynamic_self_feedback ^ #000000 :\n",
      "static * #9B59B6 --\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_formatter = ticker.FixedFormatter([\n",
    "    \"[1-5]\", \"[6-10]\", \"[11-15]\", \"[16-20]\"])\n",
    "x_locator = ticker.FixedLocator([1, 2, 3, 4])\n",
    "\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(12,4))\n",
    "ax = fig.add_subplot(111)\n",
    "for half in range(1,2+1):\n",
    "    for condition in network_conditions:\n",
    "        print(condition,markers[condition], colors[condition],linestyles[condition])\n",
    "        sns.regplot(x=\"quarter\",\n",
    "                    y=\"score_degree_corr\",\n",
    "                    data=popularity_data[(popularity_data.half==half) & (popularity_data.condition==condition)],\n",
    "                    x_estimator=np.mean,\n",
    "                    ax=ax,\n",
    "                    truncate=True,\n",
    "                    marker=markers[condition],\n",
    "                    color=colors[condition],\n",
    "                    line_kws = {'linestyle':linestyles[condition]},\n",
    "                    scatter_kws ={'s':600},\n",
    "\n",
    "                    fit_reg=True,\n",
    "                    ci=95,\n",
    "                    label=condition,\n",
    "                   )\n",
    "\n",
    "ax.set_xlabel(\"\",fontsize=label_size)\n",
    "ax.set_title('Corr(popularity, performance)',fontsize=label_size)\n",
    "ax.set_ylabel(\"\")\n",
    "#ax.set_xlim(0.5,4.5)\n",
    "ax.set_ylim(-0.09,0.99)\n",
    "\n",
    "ax.xaxis.set_major_formatter(x_formatter)\n",
    "ax.xaxis.set_major_locator(x_locator)\n",
    "\n",
    "\n",
    "ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "ax.yaxis.set_major_locator(ticker.MultipleLocator(0.2))\n",
    "\n",
    "ax.tick_params(labelsize=tick_size)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Local Adaptation:  Confidenec and Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def m(x, w):\n",
    "    \"\"\"Weighted Mean\"\"\"\n",
    "    return np.sum(x * w) / np.sum(w)\n",
    "\n",
    "def cov(x, y, w):\n",
    "    \"\"\"Weighted Covariance\"\"\"\n",
    "    return np.sum(w * (x - m(x, w)) * (y - m(y, w))) / np.sum(w)\n",
    "\n",
    "def corr(x, y, w):\n",
    "    \"\"\"Weighted Correlation\"\"\"\n",
    "    return cov(x, y, w) / np.sqrt(cov(x, x, w) * cov(y, y, w))\n",
    "\n",
    "def arrayRange(x, axis=0):\n",
    "    return np.max(x, axis=axis) - np.min(x, axis=axis)\n",
    "\n",
    "def confidence_accuracy_correlation(G):\n",
    "    error_count = 0\n",
    "    confidence = []\n",
    "    accuracy = []\n",
    "    weights = []\n",
    "    for node_name, node_data in G.nodes.data():\n",
    "        neighbors_guesses=[]\n",
    "        for neighbor in G.successors(node_name):\n",
    "            neighbors_guesses.append(G.nodes[neighbor]['independent_guess'])\n",
    "        \n",
    "        accuracy.append(1-np.abs(node_data[\"independent_error\"]))\n",
    "        \n",
    "        initial = node_data[\"independent_guess\"]\n",
    "        advice = np.array(neighbors_guesses).mean()\n",
    "        revised = node_data[\"revised_guess\"]\n",
    "        weight_on_self = (revised-advice) / (initial - advice)\n",
    "        \n",
    "        \n",
    "        weight = np.array(initial - advice)\n",
    "        #print(weight)\n",
    "        weights.append(np.abs(weight))\n",
    "        \n",
    "        confidence.append(weight_on_self)\n",
    "\n",
    "#         weights.append(np.nan)\n",
    "#         confidence.append(np.nan)\n",
    "    temp_df = pd.DataFrame({'confidence': confidence,\n",
    "                   'accuracy': accuracy, 'weights':weights})\n",
    "    \n",
    "    correlation = corr(temp_df['confidence'],temp_df['accuracy'],temp_df['weights'])\n",
    "    #correlation = corr(temp_df['confidence'],temp_df['accuracy'])\n",
    "    #correlation = pd.DataFrame({'confidence': confidence,\n",
    "    #           'accuracy': accuracy}).corr().confidence.accuracy\n",
    "\n",
    "    #print(correlation)\n",
    "           \n",
    "    #print(error_count)\n",
    "    return 0 if np.isnan(correlation) else correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "confidence = {'round_index':[],'conf_accuracy_corr':[], 'condition':[], 'game_id':[]}\n",
    "\n",
    "for condition in network_conditions:\n",
    "\n",
    "    for G in networks[condition]:\n",
    "        for rid in G.keys():\n",
    "            con_correlation = confidence_accuracy_correlation(G[rid])\n",
    "            confidence['conf_accuracy_corr'].append(con_correlation)\n",
    "            #confidence['soc_correlation'].append(soc_correlation)\n",
    "            confidence['round_index'].append(rid)\n",
    "            confidence['condition'].append(condition)\n",
    "            confidence['game_id'].append(G[rid].graph['game_id'])\n",
    "            \n",
    "            \n",
    "\n",
    "confidence_data = pd.DataFrame.from_dict(confidence)\n",
    "confidence_data['round_after_shock'] = confidence_data.round_index\n",
    "confidence_data['round_after_shock'] = np.where(confidence_data['round_index'] > 10 ,\n",
    "                                                confidence_data['round_index']-10,\n",
    "                                                confidence_data['round_after_shock']) \n",
    "confidence_data['quarter'] = None\n",
    "confidence_data.loc[confidence_data['round_index'] < 6, 'quarter'] = 1\n",
    "confidence_data.loc[(confidence_data['round_index'] >= 6) &\n",
    "                                          (confidence_data['round_index'] < 11), 'quarter'] = 2\n",
    "confidence_data.loc[(confidence_data['round_index'] >= 11) & \n",
    "                                          (confidence_data['round_index'] < 16), 'quarter'] = 3\n",
    "confidence_data.loc[confidence_data['round_index'] >= 16, 'quarter'] = 4\n",
    "\n",
    "\n",
    "confidence_data['half'] = None\n",
    "confidence_data.loc[confidence_data.round_index < 11, 'half'] = 1\n",
    "confidence_data.loc[confidence_data.round_index > 10, 'half'] = 2\n",
    "confidence_data['quarter'] = popularity_data['quarter'].astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dynamic_full_feedback p #E89468 -\n",
      "dynamic o #81B200 -\n",
      "dynamic_no_feedback d #196FFF -.\n",
      "dynamic_self_feedback ^ #000000 :\n",
      "static * #9B59B6 --\n",
      "dynamic_full_feedback p #E89468 -\n",
      "dynamic o #81B200 -\n",
      "dynamic_no_feedback d #196FFF -.\n",
      "dynamic_self_feedback ^ #000000 :\n",
      "static * #9B59B6 --\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_formatter = ticker.FixedFormatter([\n",
    "    \"[1-5]\", \"[6-10]\", \"[11-15]\", \"[16-20]\"])\n",
    "x_locator = ticker.FixedLocator([1, 2, 3, 4])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(12,4))\n",
    "ax = fig.add_subplot(111)\n",
    "for half in range(1,2+1):\n",
    "    for condition in network_conditions:\n",
    "        print(condition,markers[condition], colors[condition],linestyles[condition])\n",
    "        sns.regplot(x=\"quarter\",\n",
    "                    y=\"conf_accuracy_corr\",\n",
    "                    data=confidence_data[(confidence_data.half==half) & (confidence_data.condition==condition)],\n",
    "                    x_estimator=np.mean,\n",
    "                    ax=ax,\n",
    "                    truncate=True,\n",
    "                    marker=markers[condition],\n",
    "                    color=colors[condition],\n",
    "                    line_kws = {'linestyle':linestyles[condition]},\n",
    "                    scatter_kws ={'s':600},\n",
    "                    fit_reg=True,\n",
    "                    ci=95,\n",
    "                    label=condition,\n",
    "                   )\n",
    "\n",
    "ax.set_xlabel(\"\",fontsize=label_size)\n",
    "ax.set_title('Corr(confidence, accuracy)',fontsize=label_size)\n",
    "ax.set_ylabel(\"\")\n",
    "#ax.set_xlim(0.5,4.5)\n",
    "ax.set_ylim(-0.09,0.99)\n",
    "\n",
    "ax.xaxis.set_major_formatter(x_formatter)\n",
    "ax.xaxis.set_major_locator(x_locator)\n",
    "\n",
    "\n",
    "ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "ax.yaxis.set_major_locator(ticker.MultipleLocator(0.1))\n",
    "\n",
    "ax.tick_params(labelsize=tick_size)\n",
    "\n",
    "\n",
    "#plt.savefig('./conf_accuracy.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>round_index</th>\n",
       "      <th>conf_accuracy_corr</th>\n",
       "      <th>condition</th>\n",
       "      <th>game_id</th>\n",
       "      <th>round_after_shock</th>\n",
       "      <th>quarter</th>\n",
       "      <th>half</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4.453320e-01</td>\n",
       "      <td>dynamic_full_feedback</td>\n",
       "      <td>24BpJ5vFZLjtAx3YY</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.756550e-01</td>\n",
       "      <td>dynamic_full_feedback</td>\n",
       "      <td>24BpJ5vFZLjtAx3YY</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6.526850e-02</td>\n",
       "      <td>dynamic_full_feedback</td>\n",
       "      <td>24BpJ5vFZLjtAx3YY</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.478084e-08</td>\n",
       "      <td>dynamic_full_feedback</td>\n",
       "      <td>24BpJ5vFZLjtAx3YY</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-1.213292e-01</td>\n",
       "      <td>dynamic_full_feedback</td>\n",
       "      <td>24BpJ5vFZLjtAx3YY</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1695</th>\n",
       "      <td>16</td>\n",
       "      <td>-1.340494e-01</td>\n",
       "      <td>static</td>\n",
       "      <td>63</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1696</th>\n",
       "      <td>17</td>\n",
       "      <td>-2.949893e-01</td>\n",
       "      <td>static</td>\n",
       "      <td>63</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1697</th>\n",
       "      <td>18</td>\n",
       "      <td>8.199131e-01</td>\n",
       "      <td>static</td>\n",
       "      <td>63</td>\n",
       "      <td>8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1698</th>\n",
       "      <td>19</td>\n",
       "      <td>1.440123e-01</td>\n",
       "      <td>static</td>\n",
       "      <td>63</td>\n",
       "      <td>9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>20</td>\n",
       "      <td>-2.649785e-02</td>\n",
       "      <td>static</td>\n",
       "      <td>63</td>\n",
       "      <td>10</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1700 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      round_index  conf_accuracy_corr              condition  \\\n",
       "0               1        4.453320e-01  dynamic_full_feedback   \n",
       "1               2        2.756550e-01  dynamic_full_feedback   \n",
       "2               3        6.526850e-02  dynamic_full_feedback   \n",
       "3               4        4.478084e-08  dynamic_full_feedback   \n",
       "4               5       -1.213292e-01  dynamic_full_feedback   \n",
       "...           ...                 ...                    ...   \n",
       "1695           16       -1.340494e-01                 static   \n",
       "1696           17       -2.949893e-01                 static   \n",
       "1697           18        8.199131e-01                 static   \n",
       "1698           19        1.440123e-01                 static   \n",
       "1699           20       -2.649785e-02                 static   \n",
       "\n",
       "                game_id  round_after_shock  quarter half  \n",
       "0     24BpJ5vFZLjtAx3YY                  1      1.0    1  \n",
       "1     24BpJ5vFZLjtAx3YY                  2      1.0    1  \n",
       "2     24BpJ5vFZLjtAx3YY                  3      1.0    1  \n",
       "3     24BpJ5vFZLjtAx3YY                  4      1.0    1  \n",
       "4     24BpJ5vFZLjtAx3YY                  5      1.0    1  \n",
       "...                 ...                ...      ...  ...  \n",
       "1695                 63                  6      4.0    2  \n",
       "1696                 63                  7      4.0    2  \n",
       "1697                 63                  8      4.0    2  \n",
       "1698                 63                  9      4.0    2  \n",
       "1699                 63                 10      4.0    2  \n",
       "\n",
       "[1700 rows x 7 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confidence_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# increased individual capability explanation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does group interaction enable the group members to come up with better individual estimates, and averaging these better estimates leads to improved group performance compared to the averaged initial individual estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dynamic o\n",
      "static *\n",
      "solo_feedback H\n",
      "dynamic o\n",
      "static *\n",
      "solo_feedback H\n",
      "dynamic_no_feedback d\n",
      "dynamic_full_feedback p\n",
      "dynamic_self_feedback ^\n",
      "solo_no_feedback h\n",
      "dynamic_no_feedback d\n",
      "dynamic_full_feedback p\n",
      "dynamic_self_feedback ^\n",
      "solo_no_feedback h\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_formatter = ticker.FixedFormatter([\n",
    "    \"[1-5]\", \"[6-10]\", \"[11-15]\", \"[16-20]\"])\n",
    "x_locator = ticker.FixedLocator([1, 2, 3, 4])\n",
    "\n",
    "\n",
    "for study_number in studies:\n",
    "    fig = plt.figure(figsize=(11,6))\n",
    "    ax = fig.add_subplot(111)\n",
    "    for half in range(1,2+1):\n",
    "        for condition in round_data[round_data.study==study_number].condition.unique():\n",
    "            print(condition,markers[condition])\n",
    "            sns.regplot(x=\"quarter\",\n",
    "                        y=\"independent_error_relative2solo\",\n",
    "                        data=round_data[(round_data.half==half) & (round_data.condition==condition)],\n",
    "                        x_estimator=np.mean, ax=ax,\n",
    "                        truncate=True,\n",
    "                        marker=markers[condition],\n",
    "                        color=colors[condition],\n",
    "                        line_kws = {'linestyle':linestyles[condition]},\n",
    "                        scatter_kws ={'s':600},\n",
    "                    \n",
    "                        fit_reg=True,\n",
    "                        ci=95,\n",
    "                        label=condition,\n",
    "                       )\n",
    "            \n",
    "    ax.set_xlabel(\"\",fontsize=label_size)\n",
    "    ax.set_ylabel('Relative individual error',fontsize=label_size)\n",
    "\n",
    "    ax.set_xlim(0.5,4.5)\n",
    "    ax.set_ylim(-0.09,0.09)\n",
    "\n",
    "    ax.xaxis.set_major_formatter(x_formatter)\n",
    "    ax.xaxis.set_major_locator(x_locator)\n",
    "\n",
    "    \n",
    "    ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "    ax.yaxis.set_major_locator(ticker.MultipleLocator(0.05))\n",
    "    \n",
    "    ax.tick_params(labelsize=tick_size)\n",
    "    #plt.savefig(\"G_i_individual_s\"+str(study_number)+'.pdf', dpi=200, bbox_inches='tight', format = 'pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dynamic_full_feedback 0.6204308077596044\n",
      "dynamic 0.6250948161938205\n",
      "dynamic_self_feedback 0.30106065705168567\n",
      "dynamic_no_feedback 0.23725667263266914\n",
      "static 0.0\n"
     ]
    }
   ],
   "source": [
    "for condition in popularity_data.condition.unique():\n",
    "    print(condition ,popularity_data[popularity_data.condition==condition].score_degree_corr.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " β:  -0.3831741351269335 , t-statistic -6.515677924349985 , pvalue  7.236209212161641e-11  ci  -0.4984357415745842 -0.2679125286792828\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td>Model:</td>       <td>MixedLM</td> <td>Dependent Variable:</td> <td>score_degree_corr</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>No. Observations:</td>  <td>1300</td>         <td>Method:</td>             <td>REML</td>       \n",
       "</tr>\n",
       "<tr>\n",
       "     <td>No. Groups:</td>      <td>65</td>          <td>Scale:</td>             <td>0.0347</td>      \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Min. group size:</td>    <td>20</td>      <td>Log-Likelihood:</td>       <td>244.7305</td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Max. group size:</td>    <td>20</td>        <td>Converged:</td>             <td>Yes</td>       \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Mean group size:</td>   <td>20.0</td>            <td></td>                   <td></td>         \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                                      <td></td>                                       <th>Coef.</th> <th>Std.Err.</th>    <th>z</th>    <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                                                                  <td>0.620</td>   <td>0.042</td>  <td>14.920</td>  <td>0.000</td>  <td>0.539</td>  <td>0.702</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(condition, Treatment('dynamic_full_feedback'))[T.dynamic_no_feedback]</th>   <td>-0.383</td>   <td>0.059</td>  <td>-6.516</td>  <td>0.000</td> <td>-0.498</td> <td>-0.268</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(condition, Treatment('dynamic_full_feedback'))[T.dynamic_self_feedback]</th> <td>-0.319</td>   <td>0.059</td>  <td>-5.431</td>  <td>0.000</td> <td>-0.435</td> <td>-0.204</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(condition, Treatment('dynamic_full_feedback'))[T.static]</th>                <td>-0.620</td>   <td>0.055</td>  <td>-11.279</td> <td>0.000</td> <td>-0.728</td> <td>-0.513</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>game_id Var</th>                                                                <td>0.024</td>   <td>0.026</td>     <td></td>       <td></td>       <td></td>       <td></td>   \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary2.Summary'>\n",
       "\"\"\"\n",
       "                                        Mixed Linear Model Regression Results\n",
       "=====================================================================================================================\n",
       "Model:                              MixedLM                   Dependent Variable:                   score_degree_corr\n",
       "No. Observations:                   1300                      Method:                               REML             \n",
       "No. Groups:                         65                        Scale:                                0.0347           \n",
       "Min. group size:                    20                        Log-Likelihood:                       244.7305         \n",
       "Max. group size:                    20                        Converged:                            Yes              \n",
       "Mean group size:                    20.0                                                                             \n",
       "---------------------------------------------------------------------------------------------------------------------\n",
       "                                                                          Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
       "---------------------------------------------------------------------------------------------------------------------\n",
       "Intercept                                                                  0.620    0.042  14.920 0.000  0.539  0.702\n",
       "C(condition, Treatment('dynamic_full_feedback'))[T.dynamic_no_feedback]   -0.383    0.059  -6.516 0.000 -0.498 -0.268\n",
       "C(condition, Treatment('dynamic_full_feedback'))[T.dynamic_self_feedback] -0.319    0.059  -5.431 0.000 -0.435 -0.204\n",
       "C(condition, Treatment('dynamic_full_feedback'))[T.static]                -0.620    0.055 -11.279 0.000 -0.728 -0.513\n",
       "game_id Var                                                                0.024    0.026                            \n",
       "=====================================================================================================================\n",
       "\n",
       "\"\"\""
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forumla = \"score_degree_corr ~ C(condition, Treatment('dynamic_full_feedback'))\"\n",
    "\n",
    "\n",
    "model = smf.mixedlm(forumla,data=popularity_data[popularity_data.condition != 'dynamic'],groups='game_id').fit(method='powell')\n",
    "\n",
    "\n",
    "print(\" β: \",model.params[1], ', t-statistic',model.tvalues[1], ', pvalue ', model.pvalues[1], \n",
    "      ' ci ', model.conf_int()[0][1], model.conf_int()[1][1])#print (' ---- ')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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